1 Intro

Mapping fuel poverty, deprivation & doemstic energy use in Southampton to focus on areas which are high fp & high deprivation.

Based on:

2 Mapping Domestic Electricity consumption (LSOAs)

## 
## Attaching package: 'data.table'
## The following object is masked from 'package:raster':
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## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   `Local Authority Name` = col_character(),
##   `Local Authority Code` = col_character(),
##   `Middle Layer Super Output Area (MSOA) Name` = col_character(),
##   `Middle Layer Super Output Area (MSOA) Code` = col_character(),
##   `Lower Layer Super Output Area (LSOA) Name` = col_character(),
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##   `Total number of domestic electricity meters` = col_double(),
##   `Total domestic electricity consumption (kWh)` = col_double(),
##   `Mean domestic electricity consumption 
## (kWh per meter)` = col_double(),
##   `Median domestic electricity consumption 
## (kWh per meter)` = col_double()
## )
## Loading LSOA boundaries from file
## 
## Basingstoke and Deane        East Hampshire             Eastleigh 
##                   109                    72                    77 
##               Fareham               Gosport                  Hart 
##                    73                    53                    57 
##                Havant         Isle of Wight            New Forest 
##                    78                    89                   114 
##            Portsmouth           Southampton           Test Valley 
##                   125                   148                    71 
##            Winchester 
##                    70

Focus on Southampton

3 Mapping Domestic Gas (LSOAs)

## 
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##   `Lower Layer Super Output Area (LSOA) Code` = col_character(),
##   `Number of consuming meters` = col_double(),
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##   `Median consumption (kWh per meter)` = col_double(),
##   `Number of non-consuming meters` = col_character()
## )

Some areas are very high gas…

3.0.1 Correlating gas & elecricity

4 Mapping the Indices of Multiple Deprivation (LSOAs)

2019 data

Should be a negative correlation with gas & electricity

## 
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##   LADcd = col_character(),
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## )
## ℹ Use `spec()` for the full column specifications.

Check correlations with energy

Much stronger relationship between mean gas use & IMD score in Southampton

5 Mapping Fuel Poverty (LSOAs)

2019 Fuel Poverty

## Warning: Missing column names filled in: 'X9' [9]
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
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## Warning: 1 parsing failure.
##   row                                    col expected  actual                                                                                                                                         file
## 32845 Proportion of households fuel poor (%) a double England '/Users/ben/University of Southampton/HCC Energy Landscape Mapping project - Documents/General/data/Fuel Poverty/2019 LSOA_Fuel_Poverty.csv'

6 Correlating energy use & fuel poverty/deprivation

Figure 6.1 shows correlation of fuel poverty & IMD & energy

Correlation of IMD score, % in fuel poverty and mean energy demand (Southampton LSOAs)

Figure 6.1: Correlation of IMD score, % in fuel poverty and mean energy demand (Southampton LSOAs)

IMD & % in fuel poverty correlate. Less clear correlation with mean energy. Need to check definitions… Also we might expect more of a correlation with the Income score (although INC score seems to drive IMD score in Southampton so…)

7 Selecting areas with high fuel poverty & high deprivation

Create a local quantile for each - where are the places with higest deprivation & fuel poverty?

##     IMD_quinSoton pcFP_quinSoton  n
##  1:          <NA>           <NA>  1
##  2:   (5.75,16.5]           <NA>  1
##  3:   (5.75,16.5]          (4,8] 27
##  4:   (5.75,16.5]         (8,10]  2
##  5:   (5.75,16.5]        (10,12]  3
##  6:   (5.75,16.5]        (12,25]  3
##  7:   (16.5,25.1]          (4,8] 11
##  8:   (16.5,25.1]         (8,10] 12
##  9:   (16.5,25.1]        (10,12]  4
## 10:   (16.5,25.1]        (12,25] 10
## 11:   (25.1,36.1]          (4,8]  6
## 12:   (25.1,36.1]         (8,10] 13
## 13:   (25.1,36.1]        (10,12]  6
## 14:   (25.1,36.1]        (12,25] 12
## 15:   (36.1,67.2]          (4,8]  1
## 16:   (36.1,67.2]         (8,10]  7
## 17:   (36.1,67.2]        (10,12] 18
## 18:   (36.1,67.2]        (12,25] 11

Strange - why are there NAs?

Select them…

##      LSOA11CD IMDScore IMDDec0 IMD_quinSoton pcFP pcFP_quinSoton
##  1: E01017155   45.775       1   (36.1,67.2]   25        (12,25]
##  2: E01017156   38.486       2   (36.1,67.2]   22        (12,25]
##  3: E01017153   42.322       2   (36.1,67.2]   21        (12,25]
##  4: E01017188   42.580       2   (36.1,67.2]   20        (12,25]
##  5: E01032750   38.842       2   (36.1,67.2]   19        (12,25]
##  6: E01017218   43.096       2   (36.1,67.2]   16        (12,25]
##  7: E01017210   53.919       1   (36.1,67.2]   14        (12,25]
##  8: E01017276   36.254       2   (36.1,67.2]   14        (12,25]
##  9: E01017154   51.852       1   (36.1,67.2]   13        (12,25]
## 10: E01017216   40.447       2   (36.1,67.2]   13        (12,25]
## 11: E01017237   50.152       1   (36.1,67.2]   13        (12,25]
## Which national IMD decile are they in & what is mean % FP?
##    IMDDec0 mean_pcFP mean_IMDScore nLSOAs
## 1:       1  16.25000      50.42450      4
## 2:       2  17.85714      40.28957      7

Map them

## 
## Attaching package: 'plotly'
## The following object is masked from 'package:raster':
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## The following object is masked from 'package:graphics':
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## Warning: Ignoring unknown aesthetics: label

Note that some areas are high IMD but relatively low % fuel poverty

Figure 7.1 shows a leaflet version for pretty background to identify locations. When selected the LSOAs are coloured by their IMD Score.

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Figure 7.1: Location of filtered LSOAs (coloured by IMD score)

8 The end

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